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With the advancement in generative language models, the selection of prompts has gained significant attention in recent years. A prompt is an instruction or description provided by the user, serving as a guide for the generative language…

Machine Learning · Statistics 2024-05-21 Haoting Zhang , Jinghai He , Rhonda Righter , Zeyu Zheng

Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL). This approach offers important advantages, such as generating interpretable prompts and being compatible with black-box…

Machine Learning · Computer Science 2023-10-26 Dong-Ki Kim , Sungryull Sohn , Lajanugen Logeswaran , Dongsub Shim , Honglak Lee

Large Language Models (LLMs) perform best with well-crafted prompts, yet prompt engineering remains manual, inconsistent, and inaccessible to non-experts. We introduce Promptomatix, an automatic prompt optimization framework that transforms…

Computation and Language · Computer Science 2025-07-28 Rithesh Murthy , Ming Zhu , Liangwei Yang , Jielin Qiu , Juntao Tan , Shelby Heinecke , Caiming Xiong , Silvio Savarese , Huan Wang

Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. However, manual prompt engineering is both labor-intensive and domain-specific, necessitating…

Computation and Language · Computer Science 2024-10-04 Eshaan Agarwal , Joykirat Singh , Vivek Dani , Raghav Magazine , Tanuja Ganu , Akshay Nambi

Recent advances in Multimodal Large Language Models (MLLMs) and diffusion-based generative models have substantially improved prompt-driven image editing. However, scene text editing remains challenging, as it requires models to precisely…

Computer Vision and Pattern Recognition · Computer Science 2026-05-21 Yiheng Lin , Siyu Jiao , Xiaohan Lan , Wei Zhou , Qi She , Fei Yu , Heyun Chen , Zhengwei Wang , Jinghuan Chen , Moran Li , Yingchen Yu , Zijian Feng , Yao Zhao , Yunchao Wei , Yujie Zhong

Recent technological advances popularized the use of image generation among the general public. Crafting effective prompts can, however, be difficult for novice users. To tackle this challenge, we developed PromptMap, a new interaction…

Human-Computer Interaction · Computer Science 2025-04-04 Krzysztof Adamkiewicz , Paweł W. Woźniak , Julia Dominiak , Andrzej Romanowski , Jakob Karolus , Stanislav Frolov

Text-to-image generation has advanced rapidly, yet aligning complex textual prompts with generated visuals remains challenging, especially with intricate object relationships and fine-grained details. This paper introduces Fast Prompt…

Computation and Language · Computer Science 2024-12-12 Khalil Mrini , Hanlin Lu , Linjie Yang , Weilin Huang , Heng Wang

TIPO (Text-to-Image Prompt Optimization) introduces an efficient approach for automatic prompt refinement in text-to-image (T2I) generation. Starting from simple user prompts, TIPO leverages a lightweight pre-trained model to expand these…

Computer Vision and Pattern Recognition · Computer Science 2026-02-10 Shih-Ying Yeh , Yi Li , Sang-Hyun Park , Giyeong Oh , Xuehai Wang , Min Song , Youngjae Yu , Shang-Hong Lai

Large Language Models (LLMs) suffer from significant performance degradation when processing long contexts due to proactive interference, where irrelevant information in earlier parts of the context disrupts reasoning and memory recall.…

Computation and Language · Computer Science 2025-09-30 Mo Li , L. H. Xu , Qitai Tan , Long Ma , Ting Cao , Yunxin Liu

Text-to-image generation has advanced rapidly, but existing models still struggle with faithfully composing multiple objects and preserving their attributes in complex scenes. We propose coDrawAgents, an interactive multi-agent dialogue…

Computer Vision and Pattern Recognition · Computer Science 2026-03-16 Chunhan Li , Qifeng Wu , Jia-Hui Pan , Ka-Hei Hui , Jingyu Hu , Yuming Jiang , Bin Sheng , Xihui Liu , Wenjuan Gong , Zhengzhe Liu

Generating high-quality images without prompt engineering expertise remains a challenge for text-to-image (T2I) models, which often misinterpret poorly structured prompts, leading to distortions and misalignments. While humans easily…

Computer Vision and Pattern Recognition · Computer Science 2025-05-13 Nisan Chhetri , Arpan Sainju

Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are…

Computer Vision and Pattern Recognition · Computer Science 2025-10-09 Wen Ye , Zhaocheng Liu , Yuwei Gui , Tingyu Yuan , Yunyue Su , Bowen Fang , Chaoyang Zhao , Qiang Liu , Liang Wang

Text-to-image generative models have achieved remarkable visual quality but still struggle with compositionality$-$accurately capturing object relationships, attribute bindings, and fine-grained details in prompts. A key limitation is that…

Computer Vision and Pattern Recognition · Computer Science 2025-12-11 Arman Zarei , Jiacheng Pan , Matthew Gwilliam , Soheil Feizi , Zhenheng Yang

Text-to-Video (T2V) generation has benefited from recent advances in diffusion models, yet current systems still struggle under complex scenarios, which are generally exacerbated by the ambiguity and underspecification of text prompts. In…

Artificial Intelligence · Computer Science 2026-04-21 Chengyi Yang , Pengzhen Li , Jiayin Qi , Aimin Zhou , Ji Wu , Ji Liu

With the advancement of neural generative capabilities, the art community has increasingly embraced GenAI (Generative Artificial Intelligence), particularly large text-to-image models, for producing aesthetically compelling results.…

Human-Computer Interaction · Computer Science 2025-08-26 Aven-Le Zhou , Wei Wu , Yu-Ao Wang , Kang Zhang

When generating images from prompts that include specific entities, the model must retain as much entity-specific knowledge as possible. However, the number of entities is almost countless, and new entities emerge; memorizing all of them…

Text-to-image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized vocabulary. To address this, existing methods train rewriting models with supervision…

Computer Vision and Pattern Recognition · Computer Science 2025-12-16 Hongji Yang , Yucheng Zhou , Wencheng Han , Jianbing Shen

Recent text-to-image (T2I) models have made remarkable progress in generating visually realistic and semantically coherent images. However, they still suffer from randomness and inconsistency with the given prompts, particularly when…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Kaishen Wang , Ruibo Chen , Tong Zheng , Heng Huang

UI task automation enables efficient task execution by simulating human interactions with graphical user interfaces (GUIs), without modifying the existing application code. However, its broader adoption is constrained by the need for…

Human-Computer Interaction · Computer Science 2025-03-19 Tian Huang , Chun Yu , Weinan Shi , Zijian Peng , David Yang , Weiqi Sun , Yuanchun Shi

With AI-generated content becoming ubiquitous across the web, social media, and other digital platforms, it is vital to examine how such content are inspired and generated. The creation of AI-generated images often involves refining the…

Artificial Intelligence · Computer Science 2025-04-30 Khoi Trinh , Scott Seidenberger , Raveen Wijewickrama , Murtuza Jadliwala , Anindya Maiti